"""simple docstring"""


import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a				=logging.getLogger(__name__)






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''sequence-classification'''

				def __init__( self      ,					_lowerCamelCase ):
										if type(_lowerCamelCase ) == dict:
																lowerCamelCase__   =Namespace(**_lowerCamelCase )
										lowerCamelCase__   =glue_output_modes[hparams.task]
										lowerCamelCase__   =glue_tasks_num_labels[hparams.task]

										super().__init__(_lowerCamelCase      ,					_lowerCamelCase      ,					self.mode )

				def 						_a      ( self      ,					**_lowerCamelCase ):
										return self.model(**_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   ={"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

										if self.config.model_type not in ["distilbert", "bart"]:
																lowerCamelCase__   =batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

										lowerCamelCase__   =self(**_lowerCamelCase )
										lowerCamelCase__   =outputs[0]

										lowerCamelCase__   =self.trainer.lr_schedulers[0]["scheduler"]
										lowerCamelCase__   ={"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
										return {"loss": loss, "log": tensorboard_logs}

				def 						_a      ( self ):
										lowerCamelCase__   =self.hparams
										lowerCamelCase__   =processors[args.task]()
										lowerCamelCase__   =processor.get_labels()

										for mode in ["train", "dev"]:
																lowerCamelCase__   =self._feature_file(_lowerCamelCase )
																if os.path.exists(_lowerCamelCase ) and not args.overwrite_cache:
																						logger.info("Loading features from cached file %s"      ,					_lowerCamelCase )
																else:
																						logger.info("Creating features from dataset file at %s"      ,					args.data_dir )
																						lowerCamelCase__   =(
																						    processor.get_dev_examples(args.data_dir )
																						    if mode == "dev"
																						    else processor.get_train_examples(args.data_dir )
																						)
																						lowerCamelCase__   =convert_examples_to_features(
																						    _lowerCamelCase      ,					self.tokenizer      ,					max_length=args.max_seq_length      ,					label_list=self.labels      ,					output_mode=args.glue_output_mode      ,					)
																						logger.info("Saving features into cached file %s"      ,					_lowerCamelCase )
																						torch.save(_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = False ):
										lowerCamelCase__   ="dev" if mode == "test" else mode

										lowerCamelCase__   =self._feature_file(_lowerCamelCase )
										logger.info("Loading features from cached file %s"      ,					_lowerCamelCase )
										lowerCamelCase__   =torch.load(_lowerCamelCase )
										lowerCamelCase__   =torch.tensor([f.input_ids for f in features]      ,					dtype=torch.long )
										lowerCamelCase__   =torch.tensor([f.attention_mask for f in features]      ,					dtype=torch.long )
										lowerCamelCase__   =torch.tensor([f.token_type_ids for f in features]      ,					dtype=torch.long )
										if self.hparams.glue_output_mode == "classification":
																lowerCamelCase__   =torch.tensor([f.label for f in features]      ,					dtype=torch.long )
										elif self.hparams.glue_output_mode == "regression":
																lowerCamelCase__   =torch.tensor([f.label for f in features]      ,					dtype=torch.float )

										return DataLoader(
										    TensorDataset(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )      ,					batch_size=_lowerCamelCase      ,					shuffle=_lowerCamelCase      ,					)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   ={"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

										if self.config.model_type not in ["distilbert", "bart"]:
																lowerCamelCase__   =batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

										lowerCamelCase__   =self(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =outputs[:2]
										lowerCamelCase__   =logits.detach().cpu().numpy()
										lowerCamelCase__   =inputs["labels"].detach().cpu().numpy()

										return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =torch.stack([x["val_loss"] for x in outputs] ).mean().detach().cpu().item()
										lowerCamelCase__   =np.concatenate([x["pred"] for x in outputs]      ,					axis=0 )

										if self.hparams.glue_output_mode == "classification":
																lowerCamelCase__   =np.argmax(_lowerCamelCase      ,					axis=1 )
										elif self.hparams.glue_output_mode == "regression":
																lowerCamelCase__   =np.squeeze(_lowerCamelCase )

										lowerCamelCase__   =np.concatenate([x["target"] for x in outputs]      ,					axis=0 )
										lowerCamelCase__   =[[] for _ in range(out_label_ids.shape[0] )]
										lowerCamelCase__   =[[] for _ in range(out_label_ids.shape[0] )]

										lowerCamelCase__   ={**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task      ,					_lowerCamelCase      ,					_lowerCamelCase )}

										lowerCamelCase__   =dict(results.items() )
										lowerCamelCase__   =results
										return ret, preds_list, out_label_list

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self._eval_end(_lowerCamelCase )
										lowerCamelCase__   =ret["log"]
										return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self._eval_end(_lowerCamelCase )
										lowerCamelCase__   =ret["log"]
										# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
										return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






				@staticmethod
				def 						_a      ( _lowerCamelCase      ,					_lowerCamelCase ):
										BaseTransformer.add_model_specific_args(_lowerCamelCase      ,					_lowerCamelCase )
										parser.add_argument(
										    "--max_seq_length"      ,					default=128      ,					type=_lowerCamelCase      ,					help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )      ,					)

										parser.add_argument(
										    "--task"      ,					default=""      ,					type=_lowerCamelCase      ,					required=_lowerCamelCase      ,					help="The GLUE task to run"      ,					)
										parser.add_argument(
										    "--gpus"      ,					default=0      ,					type=_lowerCamelCase      ,					help="The number of GPUs allocated for this, it is by default 0 meaning none"      ,					)

										parser.add_argument(
										    "--overwrite_cache"      ,					action="store_true"      ,					help="Overwrite the cached training and evaluation sets" )

										return parser





def      lowerCamelCase_					(   )				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =argparse.ArgumentParser()
						add_generic_args(__lowerCAmelCase  ,       os.getcwd()							)
						lowerCamelCase__   =GLUETransformer.add_model_specific_args(__lowerCAmelCase  ,       os.getcwd()							)
						lowerCamelCase__   =parser.parse_args()

						# If output_dir not provided, a folder will be generated in pwd
						if args.output_dir is None:
												lowerCamelCase__   =os.path.join(
												    "./results"  ,       F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'							)}'''  ,       )
												os.makedirs(args.output_dir							)

						lowerCamelCase__   =GLUETransformer(__lowerCAmelCase							)
						lowerCamelCase__   =generic_train(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Optionally, predict on dev set and write to output_dir
						if args.do_predict:
												lowerCamelCase__   =sorted(glob.glob(os.path.join(args.output_dir  ,       "checkpoint-epoch=*.ckpt"							)  ,       recursive=__lowerCAmelCase							)							)
												lowerCamelCase__   =model.load_from_checkpoint(checkpoints[-1]							)
												return trainer.test(__lowerCAmelCase							)


if __name__ == "__main__":
	main()



"""simple docstring"""


from __future__ import annotations

from typing import Any






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = 0 ):
										lowerCamelCase__   , lowerCamelCase__   =row, column
										lowerCamelCase__   =[[default_value for c in range(_lowerCamelCase )] for r in range(_lowerCamelCase )]

				def __str__( self ):
										lowerCamelCase__   =F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

										# Make string identifier
										lowerCamelCase__   =0
										for row_vector in self.array:
																for obj in row_vector:
																						lowerCamelCase__   =max(_lowerCamelCase      ,					len(str(_lowerCamelCase ) ) )
										lowerCamelCase__   =F'''%{max_element_length}s'''

										# Make string and return
										def single_line(_lowerCamelCase ) -> str:
																nonlocal string_format_identifier
																lowerCamelCase__   ="["
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
																line += "]"
																return line

										s += "\n".join(single_line(_lowerCamelCase ) for row_vector in self.array )
										return s

				def __repr__( self ):
										return str(self )

				def 						_a      ( self      ,					_lowerCamelCase ):
										if not (isinstance(_lowerCamelCase      ,					(list, tuple) ) and len(_lowerCamelCase ) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True

				def __getitem__( self      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										return self.array[loc[0]][loc[1]]

				def __setitem__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										lowerCamelCase__   =value

				def __add__( self      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == another.row and self.column == another.column

										# Add
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c] + another[r, c]
										return result

				def __neg__( self ):
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =-self[r, c]
										return result

				def __sub__( self      ,					_lowerCamelCase ):
										return self + (-another)

				def __mul__( self      ,					_lowerCamelCase ):

										if isinstance(_lowerCamelCase      ,					(int, float) ):  # Scalar multiplication
																lowerCamelCase__   =Matrix(self.row      ,					self.column )
																for r in range(self.row ):
																						for c in range(self.column ):
																												lowerCamelCase__   =self[r, c] * another
																return result
										elif isinstance(_lowerCamelCase      ,					_lowerCamelCase ):  # Matrix multiplication
																assert self.column == another.row
																lowerCamelCase__   =Matrix(self.row      ,					another.column )
																for r in range(self.row ):
																						for c in range(another.column ):
																												for i in range(self.column ):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																lowerCamelCase__   =F'''Unsupported type given for another ({type(_lowerCamelCase )})'''
																raise TypeError(_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =Matrix(self.column      ,					self.row )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c]
										return result






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase ) and isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										lowerCamelCase__   =v.transpose()
										lowerCamelCase__   =(v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							lowerCamelCase__   =Matrix(3  ,       3  ,       0							)
							for i in range(3							):
													lowerCamelCase__   =1
							print(F'''a^(-1) is {ainv}'''							)
							# u, v
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =1, 2, -3
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =4, -2, 5
							print(F'''u is {u}'''							)
							print(F'''v is {v}'''							)
							print(F'''uv^T is {u * v.transpose()}'''							)
							# Sherman Morrison
							print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase  ,       __lowerCAmelCase							)}'''							)





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							import doctest

							doctest.testmod()





	testa()


